Technical Field
Example aspects described herein generally relate to automated systems for predicting breakout artists and media content.
Background
The ability to consistently predict the next breakout success in music, film, or the arts has long been a holy grail of media-related industries. Media companies rely on predictions to identify talent and evaluate business deals, while consumers often take joy in discovering songs and movies before they find mainstream popularity.
Due to the subjective nature of media, predictions of breakout success require human insights and domain expertise. Journalism has typically played the role of providing these human insights. Editorial content such as news articles, reviews and interviews typically provide the most meaningful indicators of which new artists and content will have broad appeal.
More recently, the growth of the Internet and the wide adoption of social technologies to share and discuss media have enabled access to a limitless source of editorial content and human insights for better predicting the next big success.
One difficulty with relying on human insights to predict breakout success is that applying such insights to large catalogs of media content in a consistent and/or objective manner is not possible without the use of technology. Today, streaming services have become one of the most popular methods by which media content is distributed to consumers. Media streaming services typically provide subscription access to a catalog of millions of songs, films or television shows, and the most successful media streaming services deliver content globally to millions of consumers. There has yet to be a technical solution for applying insights gleaned from editorial content to large portions of these catalogs.